Dr. Wadii Boulila
Robotics and Internet of Things Research Lab, College of Computer and Information Systems, Prince Sultan University, Saudi Arabia
E-mail: wboulila@psu.edu.sa


Dr. Jawad Ahmad
School of Computing, Edinburgh Napier University, UK
E-mail: j.ahmad@napier.ac.uk


Dr. Maha Driss
Security Engineering Lab, College of Computer and Information Systems, Prince Sultan University, Saudi Arabia
E-mail: mdriss@psu.edu.sa


Prof. Anis Koubaa
Robotics and Internet of Things Research Lab, College of Computer and Information Systems, Prince Sultan University, Saudi Arabia
E-mail: akoubaa@psu.edu.sa


Prof. Mark Elliot
Social Statistics Department, School of Social Sciences, University of Manchester, UK
E-mail: mark.elliot@manchester.ac.uk

Recent advances in Deep Learning (DL) and the Internet of Things (IoT) led to remarkable results in many fields related to computer vision. The last decade witnessed the explosion of IoT systems with several applications thanks to the progress of sensors and communication technologies. Traditional artificial intelligence techniques showed their limitation in handling complex and multi-modal patterns extracted for the big data generated by new IoT systems. In this context, recent trends of DL techniques such as (convolutional neural networks, stacked autoencoders, reinforcement learning, adversarial learning, meta-learning, recurrent neural networks, self-supervised learning, transformers, and graph neural networks) have emerged as a promising solution to achieve high performance in terms of accuracy and processing speed while being applicable to complex data coming from IoT systems. Applications of using IoT systems empowered by DL can cover many computer vision domains such as transportation, healthcare, agriculture, and manufacturing.

The scope of the DICV 2023 includes, but is not limited to, the following topics:

• DL-based methods for IoT systems with a focus on computer vision applications
• Recent trends of DL techniques (convolutional neural networks, stacked autoencoders, reinforcement learning, adversarial learning, meta-learning, recurrent neural networks, self-supervised learning, transformers, and graph neural networks) for computer vision applications
• DL for complex (multi-modal, high dimensional, homogeneous, multivariate, uncertain, massive, and missing) data
• Security and privacy in IoT-based applications using DL techniques
• Federated/distributed learning-based applications for computer vision and IoT systems
• Real-world application of DL techniques for IoT applications (e.g., transportation, healthcare, agriculture, and manufacturing)